Moving target focusing method and system based on generative adversarial network

ABSTRACT

A moving target focusing method and system based on a generative adversarial network are provided. The method includes: generating, using a Range Doppler algorithm, a two-dimensional image including at least one defocused moving target, as a training sample; generating at least one ideal Gaussian point in a position of at least one center of the at least one defocused moving target in the two-dimensional image, to generate a training label; constructing the generative adversarial network, the generative adversarial network includes a generative network and a discrimination network; inputting the training sample and the training label into the generative adversarial network to perform repeated training until an output of the generative network reaches a preset condition, to thereby obtain a trained network model; and inputting a testing sample into the trained network model, to output a moving target focused image.

TECHNICAL FIELD

The disclosure relates to a field of moving target focusing technology,and in particular to a moving target focusing method and system based ona generative adversarial network.

BACKGROUND

In order to achieve focusing moving targets in Synthetic Aperture Radar(SAR) images, a traditional method is to estimate motion parameters ofthe moving targets, to complete a range migration correction of themoving targets by an estimated range velocity, and to construct anazimuth-matching filter function by an estimated azimuth velocity,thereby to complete the moving target focusing.

Conventional moving target imaging algorithms require estimation of theparameters for each of the moving targets before focusing. When thereare multiple moving targets and each of the moving targets has differentvelocities, these moving targets need to be processed separately, whichis a tedious process.

SUMMARY

In view of the above, the disclosure aims to provide a moving targetfocusing method and system based on a generative adversarial network.The focusing method uses the generative adversarial network to achievefocusing of at least one defocused moving target in an SAR image.

In order to achieve the above objectives, the disclosure providestechnical solutions as follows.

The disclosure provides the moving target focusing method based on agenerative adversarial network, including the following steps:generating a two-dimensional image including at least one defocusedmoving target by a Range Doppler algorithm as a training sample andgenerating a training label with at least one ideal Gaussian pointcorresponding to the at least one defocused moving target at a center ofthe at least one defocused moving target; constructing the generativeadversarial network, which includes a generative network and adiscrimination network; inputting the training sample to the generativenetwork, to generate a generated image similar to the training label;inputting the generated image and the training label into thediscrimination network, to obtain a discrimination result, and returningthe discrimination result to the generative network; inputting thetraining sample and the training label into the generative adversarialnetwork to perform repeated training until an output of the generativenetwork reaches a preset condition, to thereby obtain a trained networkmodel; and inputting a testing sample into the trained network model, tooutput a moving target focused image.

In an embodiment of the disclosure, the discrimination network is amulti-layered convolution network.

In an embodiment of the disclosure, the training label is a noiselessimage with the at least one ideal Gaussian point.

In an embodiment of the disclosure, the generative network is a Unetnetwork based on a residual structure; the residual structure includes aconvolution residual block and an identity residual block; theconvolution residual block is configured to adjust a size and a channelnumber of a feature diagram; and the identity residual block isconfigured to increase a depth of the generative network.

In an embodiment of the disclosure, the convolution residual blockincludes three three-layered structures; the three three-layeredstructures include a first three-layered structure, a secondthree-layered structure, and a third three-layered structure stacked insequence, each of the three three-layered structures includes aconvolution layer, a batch normalization and an activation functionstacked in sequence; the convolution residual block further includes atwo-layered structure, and the two-layered structure includes aconvolution layer and a batch normalization stacked in sequence; aninput end of the convolution layer of the two-layered structure isconnected to an input end of the first three-layered structure, and anoutput end of the batch normalization of the two-layered structure isconnected between the batch normalization of the third three-layeredstructure and the activation function of the third three-layeredstructure, such which is configured to form a skip connection; theconvolution residual block is configured to adjust the size and thechannel number of the feature diagram and to prevent gradientdisappearance or gradient explosion caused by that the depth of thegenerative network is oversized.

In an embodiment of the disclosure, the identity residual block includestwo three-layered structures; the two three-layered structures include afirst three-layered structure and a second three-layered structurestacked one another; each of the two three-layered structure includes aconvolution layer, a batch normalization, and an activation function; aninput end of the first three-layered structure is connected between thebatch normalization of the second three-layered structure and theactivation function of the second three-layered structure, such which isconfigured to form a skip connection; the identity residual block isconfigured to increase the depth of the generative network and toprevent gradient disappearance or gradient explosion caused by that thedepth of the generative network is oversized.

In an embodiment of the disclosure, the generative network includes adown-sampling structure, an up-sampling structure, and a connectionstructure for connecting a feature diagram of the down-samplingstructure and a feature diagram of the up-sampling structure; and thedown-sampling structure includes a residual block, and the residualblock is used for down-sampling.

In an embodiment of the disclosure, the discrimination network includesfive convolution layers, three batch normalizations and four rectifiedlinear unit (relu) activation functions, and an output of a lastconvolution layer of the five convolution layers is input into a sigmoidfunction.

The moving target focusing system based on a generative adversarialnetwork provided by the disclosure includes a memory, a processor and acomputer program stored in the memory and executable by the processor,and the processor implements the moving target focusing method describedabove upon executing the computer program.

Beneficial effects of the disclosure are as follows.

According to the moving target focusing method and the system thereofbased on the generative adversarial network provided by the disclosure,a two-dimensional image including the defocused moving targets isgenerated by the Range Doppler algorithm as a training sample; atraining label with the ideal Gaussian points at the centers of thedefocused moving targets is generated; a generated image close to thetraining label is obtained by the generative adversarial network; adiscrimination result is obtained through the discrimination network,and returned to the generative network; the training sample and thetraining label are input into the generative adversarial network toperform repeated training until an output of the generative networkreaches a preset condition, to thereby obtain a trained network model;and a moving target focused image is output by using the trained networkmodel.

Compared with the conventional SAR moving target imaging algorithms, themethod of the disclosure avoids the estimation of parameters for each ofthe moving targets followed by the range migration correction and theazimuth-matching filter separately. And the method of the disclosure candirectly process the multiple defocused moving targets into well-focusedtargets at the same time.

In a traditional SAR data processing method, both of noise eliminationand target focusing are required to be performed separately. The methodof the disclosure can achieve the moving target focusing whileeliminating noise through a trained network model, which achieves noiseelimination and target focusing simultaneously.

Other advantages, objects and features of the disclosure will to someextent be set forth in the subsequent specification and to some extentwill be apparent to those skilled in the related art based on anexamination of the following research or can be taught from the practiceof the disclosure. The objects and other advantages of the disclosuremay be realized and obtained by the following specification.

BRIEF DESCRIPTION OF DRAWINGS

In order to make the objectives, technical solutions and beneficialeffects of the disclosure clearer, the disclosure provides followingattached drawings for description.

FIG. 1 illustrates a schematic structural diagram of a generativeadversarial network according to an embodiment of the disclosure.

FIGS. 2A-2B show training data of a training sample according to anembodiment of the disclosure.

FIG. 3 illustrates a schematic structural diagram of an identityresidual block according to an embodiment of the disclosure.

FIG. 4 illustrates a schematic structural diagram of a convolutionresidual block according to an embodiment of the disclosure.

FIG. 5 illustrates a schematic structural diagram of a generativenetwork according to an embodiment of the disclosure.

FIG. 6 illustrates a schematic structural diagram of a discriminationnetwork according to an embodiment of the disclosure.

FIGS. 7A-7C show a training sample, a training result and a traininglabel under a SNR (referred to a signal-noise ratio) of 30 dB whiteGaussian noise according to an embodiment of the disclosure.

FIGS. 8A-8C show a training sample, a training result and a traininglabel under a SNR of 20 dB white Gaussian noise according to anembodiment of the disclosure.

FIGS. 9A-9C show a training sample, a training result and a traininglabel under a SNR of 10 dB white Gaussian noise according to anembodiment of the disclosure.

FIGS. 10A-10C show a training sample, a training result and a traininglabel under a SNR of 0 dB white Gaussian noise according to anembodiment of the disclosure.

FIGS. 11A-11C show a testing sample, a testing result and a testinglabel of a trained network model according to an embodiment of thedisclosure.

FIGS. 12A-12C show a testing sample, a testing result and a testinglabel of the trained network model according to another embodiment ofthe disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

The disclosure is further described below with reference to the attacheddrawings and illustrated embodiments, so that those skilled in therelated field may better understand and implement the disclosure, butthe embodiments mentioned are not limited to the disclosure.

As shown in FIG. 1 , the embodiment provides a moving target focusingmethod based on a generative adversarial network (GAN), and the methoduses the generative adversarial network to achieve moving targetfocusing, which can realize processing multiple targets with differentmotion parameters simultaneously. A well-focused image for the movingtargets is directly achieved from the defocused moving targets in theSAR image without the estimation of parameters. The method includes thefollowing steps.

First, a synthetic aperture radar (SAR) image containing 1-3 defocusedmoving targets is simulated by Range Doppler (RD) algorithm, and theimage is used as a training sample of the generative adversarialnetwork. A training label of the generative adversarial network isobtained with 1-3 ideal Gaussian points corresponding to the 1-3defocused moving targets at centers of the 1-3 defocused moving targets.

Then, the generative adversarial network is constructed, including agenerative network and a discrimination network. The generative networkis used to generate an image close to the training label to serve as aninput of the discrimination network according to the input trainingsample.

The discrimination network is a five-layered convolutional network, andis used to determine a discrimination result, where an output of thegenerative network is determined as “fake”, and an output of thetraining label is determined as “real”, and the discrimination result isreturned to the generative network, and a specific schematic diagram ofthe generative adversarial network is as shown in FIG. 1 , where Drepresents the discrimination network, and G represents the generativenetwork.

The specific steps of generating the training and testing data in thegenerative adversarial network are as follows.

A two-dimensional image for defocused moving targets is generated by theRange Doppler algorithm as an input training sample of the generativeadversarial network. Each the input training sample includes 1-3defocused moving targets that differ in range and azimuth velocitycomponents. In order to generate a relatively obvious two-dimensionalimage for the defocused moving targets, a design of simulationparameters is as shown in Table 1.

TABLE 1 Simulation parameters Parameter Value PRF 800 Hz Carrierwaveform 9.6 GHz Platform velocity 60 m/s Platform height 100 m Targetrange velocity (25 m/s-35 m/s) Target azimuth velocity  (5 m/s-15 m/s)

1-3 ideal Gaussian points corresponding to the 1-3 defocused movingtargets are generated at the centers of the 1-3 defocused moving targetsin the two-dimensional image as a training label of the generativeadversarial network. Both the input training sample and the traininglabel are converted into 256*256 matrixes to facilitate inputting aneural network for training, as shown in FIG. 2 , where FIG. 2A is thetraining sample of the training data, and FIG. 2B is the training labelof the training data.

In total, 2000 couples of training samples and labels are generated asshown in FIG. 1 . Each of the 2000 input training samples isrespectively added with white Gaussian noise with the SNR (referred to asignal-noise ratio) of 30 dB, 20 dB, 10 dB and 0 dB, and finally, 8000images for the defocused moving targets with different SNR of the whiteGaussian noise are obtained as the training samples of the generativeadversarial network. Ideal Gaussian points without any noise are used asthe training label. Data of the testing samples are generated in aconsistent method with the training samples, to generate a total of 200images for the defocused moving targets, and a random white Gaussiannoise within a range from 0 dB to 30 dB is added to each of the 200images as one of the testing samples.

A training process of the generative adversarial network is specificallyas follows.

The generative network is a Unet network based on a residual structure,where the residual structure includes a convolution residual block, suchas Conv_block, and an identity residual block, such as Identity_block.The convolution residual block is mainly used to adjust the size and thechannel number of a feature map. The specific structures thereof areshown in FIG. 3 , FIG. 4 and FIG. 5 .

The convolution residual block in the embodiment includes threeconvolutional layers (also referred to as conv), three batchnormalization layers (also referred to as batch_norm) and threeactivation functions (also referred to as ReLU), a second three-layeredstructure and a third three-layered structure stacked in sequence, whereeach of the three three-layered structures include a convolutional layer(also referred to as cony), a batch normalization layer (also referredto as batch_norm), and an activation functions (also referred to asrelu) stacked in sequence. A two-layered structure includes aconvolution layer and a batch normalization. And an input end of theconvolution layer of the two-layered structure is connected to an inputend of the first three-layered structure, and an output end of the batchnormalization of the two-layered structure is connected between thebatch normalization of the third three-layered structure and the reluactivation function of the third three-layered structure, such which isregarded as a skip connection structure. The convolution residual blockis used to adjust the size and the channel number of the feature diagramand to prevent a phenomenon of gradient disappearance or gradientexplosion caused by that the depth of the generative network isoversized.

The identity residual block in the embodiment includes two three-layeredstructures, where the two three-layered structures include a firstthree-layered structure and a second three-layered structure; each ofthe two three-layered structures include a convolution layer, a batchnormalization, and a relu activation function. An input end of the firstthree-layered structure is connected between the batch normalization ofthe second three-layered structure and the relu activation function ofthe second three-layered structure, such which is regarded as a skipconnection structure. The identity residual block is used to increasethe depth of the generative network, and to prevent a phenomenon ofgradient disappearance or gradient explosion caused by that the depth ofthe generative network is oversized.

The generative network in the embodiment includes a down-samplingstructure, an up-sampling structure, and a connection structure forconnecting a feature diagram of the down-sampling structure and afeature diagram of the up-sampling structure. The down-samplingstructure includes a residual block, which is used for down-sampling.The identity_block shown in FIG. 5 is the structure in FIG. 3 ; and thecony block shown in FIG. 5 is the structure in FIG. 4 .

The discrimination network provided in the embodiment includes fiveconvolution layers, and an output of a last convolution layer of thefive convolution layers is input into a Sigmoid function to output thediscrimination result of the discrimination network. The specificstructure thereof is shown in FIG. 6 .

The discrimination network in the embodiment includes five convolutionlayers, three batch normalizations and four relu activation functions,and a last layer of the four relu activation functions outputs adiscrimination probability by using the Sigmoid function.

The training samples and training labels are input the generativeadversarial network to be trained for 100 rounds to output anintermediate result, and the training process is stopped when the outputof the generative network meets the preset condition and a trainednetwork model is saved. The final trained network model has both of thenoise elimination and target focusing functions. Therefore, the trainedgenerative adversarial network is completed when the output of thegenerative network can achieve removing background noise with differentintensities and the moving target focusing with different defocusedextent.

The training results of the training samples according to the generativeadversarial network are shown in FIGS. 7A-7C, FIGS. 8A-8C, FIGS. 9A-9Cand FIGS. 10A-10C.

FIG. 7A illustrates the training sample with an addition of the 30 dBwhite Gaussian noise; FIG. 7B is an output focused image of thegenerative adversarial network, which is very similar to the traininglabel shown in FIG. 7C. Therefore, the moving targets with differentdefocused extent are able to be focused through the generativeadversarial network, and the noise in the image is also suppressed.

FIG. 8A illustrates the training sample with an addition of the 20 dBwhite Gaussian noise; FIG. 8B is an output focused image of thegenerative adversarial network, which is very similar to the traininglabel shown in FIG. 8C. Therefore, the moving targets with differentdefocused extent are able to be focused through the generativeadversarial network, and the noise in the image is suppressed.

FIG. 9A illustrates the training sample with an addition of the 10 dBwhite Gaussian noise; FIG. 9B is an output focused image of thegenerative adversarial network, which is very similar to the traininglabel shown in FIG. 9C. Therefore, the moving targets with differentdefocused extent are able to be focused through the generativeadversarial network, and the noise in the image is suppressed.

FIG. 10A illustrates the training sample with an addition of the 0 dB ofwhite Gaussian noise; FIG. 10B is an output focused image of thegenerative adversarial network, which is very similar to the traininglabel shown in FIG. 10C. Therefore, the moving targets with differentdefocused extent are able to be focused through the generativeadversarial network, and the noise in the image is suppressed.

The difference between the testing samples and the training samples,which are provided by the embodiments of the disclosure during a test ofthe generative adversarial network, is the addition of background noise.The training samples are added with the white Gaussian noise in fourdetermined intensities, and the testing samples are added with therandom white Gaussian noise in a range from 0 dB to 30 dB. The trainednetwork model saved from the training process is tested by inputting thetesting samples to obtain output focused images and the output focusedimages are compared with the testing labels.

The results of the testing samples are shown in FIGS. 11A-11C and FIGS.12A-12C. FIG. 11A shows the testing sample; FIG. 11B is the outputfocused image of the trained network model; FIG. 11C is the testinglabel. FIG. 12A is the testing sample; FIG. 12B is the focused outputimage of the trained network model; FIG. 12C is the testing label of thetesting sample. Therefore, it can be seen that the trained network modelcan achieve the background noise elimination and moving target focusingeven for the testing samples that have not been encountered in thetraining process.

When the generated image in the generative network and the correspondinglabel are together input the discrimination network and thediscrimination network determines that the generated image and thecorresponding label are a pair of real images, the discrimination resultof the discriminant network outputs a probability of 1, which means thatthe generated image of the generative network has successfully deceivedthe discrimination network. If the discrimination network determinesthat the generated image and the corresponding label are not a pair ofreal images, the discrimination result of the discrimination networkoutputs a smaller probability. When the discrimination network outputsthe smaller probability, the generative network will continuously adjustthe parameters of the trained network model in order to improve theprobability of the discrimination network to achieve the purpose offaking the real. A function to calculate a loss of the generativeadversarial network is divided into two parts, one is to calculate amean square error between the generated image of the generative networkand the label, in order to make the generative network output as closeas possible to the label, the loss of this part should be as small aspossible. Another part is the output probability of the discriminationnetwork, and the value of this part should be as large as possible. Whenboth parts reach the optimum, it means that the output of the generativenetwork is very close to the label, at this time the background noiseshould have been eliminated, and the part of the moving targets leftshould have achieved focusing. Otherwise, the trained network has notreached the optimum and the training process need continue.

The above described embodiments are merely the illustrated embodimentsfor fully describing the disclosure and the scope of the protection ofthe disclosure is not limited thereto. Any equivalent substitutions ortransformations made by those skilled in the related art on the basis ofthe disclosure are within the scope of the protection of the disclosure.The scope of the protection of the disclosure is subject to the claims.

What is claimed is:
 1. A moving target focusing method based on agenerative adversarial network, comprising: generating, using a RangeDoppler algorithm, a two-dimensional image comprising at least onedefocused moving target, as a training sample; generating at least oneideal Gaussian point in a position of at least one center of the atleast one defocused moving target in the two-dimensional image, togenerate a training label, the at least one ideal Gaussian point beingcorresponding to the at least one defocused moving target one-to-one;constructing the generative adversarial network, wherein the generativeadversarial network comprises a generative network and a discriminationnetwork; inputting the training sample and the training label into thegenerative adversarial network to perform repeated training until anoutput of the generative network reaches a preset condition, to therebyobtain a trained network model, comprising: inputting the trainingsample to the generative network, to generate a generated image similarto the training label; and inputting the generated image and thetraining label into the discrimination network, to obtain adiscrimination result, and returning the discrimination result to thegenerative network; and inputting a testing sample into the trainednetwork model, to output a moving target focused image.
 2. The movingtarget focusing method based on a generative adversarial networkaccording to claim 1, wherein the discrimination network is amulti-layered convolution network.
 3. The moving target focusing methodbased on a generative adversarial network according to claim 1, whereinthe training label is a noiseless image with the at least one idealGaussian point.
 4. The moving target focusing method based on agenerative adversarial network according to claim 1, wherein thegenerative network is a Unet network based on a residual structure; theresidual structure comprises a convolution residual block and anidentity residual block; the convolution residual block is configured toadjust a size and a channel number of a feature diagram; and theidentity residual block is configured to increase a depth of thegenerative network.
 5. The moving target focusing method based on agenerative adversarial network according to claim 4, wherein theconvolution residual block comprises three three-layered structures; thethree three-layered structures comprise a first three-layered structure,a second three-layered structure, and a third three-layered structurestacked in sequence, each of the three three-layered structurescomprises a convolution layer, a batch normalization and an activationfunction stacked in sequence; wherein the convolution residual blockfurther comprises a two-layered structure, the two-layered structurecomprises a convolution layer and a batch normalization stacked insequence; an input end of the convolution layer of the two-layeredstructure is connected to an input end of the first three-layeredstructure, and an output end of the batch normalization of thetwo-layered structure is connected between the batch normalization ofthe third three-layered structure and the activation function of thethird three-layered structure; and wherein the convolution residualblock is configured to prevent one of gradient disappearance andgradient explosion caused by that the depth of the generative network isoversized.
 6. The moving target focusing method based on a generativeadversarial network according to claim 4, wherein the identity residualblock comprises two three-layered structures, the two three-layeredstructures comprise a first three-layered structure and a secondthree-layered structure stacked in sequence, each of the twothree-layered structure comprises a convolution layer, a batchnormalization, and an activation function; wherein an input end of thefirst three-layered structure is connected between the batchnormalization of the second three-layered structure and the activationfunction of the second three-layered structure; and wherein the identityresidual block is configured to prevent one of gradient disappearanceand gradient explosion caused by that the depth of the generativenetwork is oversized.
 7. The moving target focusing method based on agenerative adversarial network according to claim 1, wherein thegenerative network comprises a down-sampling structure, an up-samplingstructure, and a connection structure for connecting a feature diagramof the down-sampling structure and a feature diagram of the up-samplingstructure; and the down-sampling structure comprises a residual block,and the residual block is configured for down-sampling.
 8. The movingtarget focusing method based on a generative adversarial networkaccording to claim 1, wherein the discrimination network comprises fiveconvolution layers, three batch normalizations and four rectified linearunit (relu) activation functions, and an output of a last convolutionlayer of the five convolution layers is input into a Sigmoid function.9. A moving target focusing system based on a generative adversarialnetwork, comprising: a memory; a processor; and a computer programstored on the memory and executable by the processor; wherein theprocessor is configured to implement the moving target focusing methodaccording to claim 1 upon executing the computer program.